Overview

Dataset statistics

Number of variables37
Number of observations4013
Missing cells32571
Missing cells (%)21.9%
Duplicate rows7
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory336.9 B

Variable types

Categorical26
Text8
Numeric3

Alerts

Dataset has 7 (0.2%) duplicate rowsDuplicates
n_estancias is highly overall correlated with n_visitasHigh correlation
n_visitas is highly overall correlated with n_estanciasHigh correlation
year is highly overall correlated with ¿? and 1 other fieldsHigh correlation
paliativo_onc_noc is highly overall correlated with paliativo_no_onc_noc and 2 other fieldsHigh correlation
paliativo_no_onc_noc is highly overall correlated with paliativo_onc_noc and 1 other fieldsHigh correlation
p_terminal is highly overall correlated with paliativo_onc_nocHigh correlation
agonia is highly overall correlated with sedacionHigh correlation
sedacion is highly overall correlated with agoniaHigh correlation
cronico_reag is highly overall correlated with paliativo_onc_noc and 1 other fieldsHigh correlation
¿? is highly overall correlated with yearHigh correlation
toracocen is highly overall correlated with yearHigh correlation
h_procedencia is highly imbalanced (61.0%)Imbalance
ap is highly imbalanced (67.3%)Imbalance
otros is highly imbalanced (91.3%)Imbalance
paliativo_onc_noc is highly imbalanced (51.8%)Imbalance
fiebre is highly imbalanced (71.1%)Imbalance
gds_fast is highly imbalanced (68.1%)Imbalance
eva_ing is highly imbalanced (80.5%)Imbalance
sedacion is highly imbalanced (83.7%)Imbalance
transfusion is highly imbalanced (89.0%)Imbalance
paracentesis is highly imbalanced (81.2%)Imbalance
toracocentesis is highly imbalanced (97.8%)Imbalance
¿? is highly imbalanced (94.2%)Imbalance
toracocen is highly imbalanced (98.9%)Imbalance
otros has 1055 (26.3%) missing valuesMissing
otros_1 has 2464 (61.4%) missing valuesMissing
p_terminal has 2466 (61.5%) missing valuesMissing
agonia has 1058 (26.4%) missing valuesMissing
eva_ing has 2465 (61.4%) missing valuesMissing
otros_2 has 2465 (61.4%) missing valuesMissing
otros_complicaciones has 1057 (26.3%) missing valuesMissing
agudo_estable has 1551 (38.6%) missing valuesMissing
cronico_reag has 1550 (38.6%) missing valuesMissing
transfusion has 1550 (38.6%) missing valuesMissing
paracentesis has 1550 (38.6%) missing valuesMissing
toracocentesis has 2606 (64.9%) missing valuesMissing
ayuntamiento has 1551 (38.6%) missing valuesMissing
fecha_alta has 3247 (80.9%) missing valuesMissing
¿? has 2959 (73.7%) missing valuesMissing
toracocen has 2958 (73.7%) missing valuesMissing

Reproduction

Analysis started2023-06-29 14:03:07.801996
Analysis finished2023-06-29 14:03:15.185097
Duration7.38 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

h_procedencia
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
clinico
2240 
no
1251 
conxo
329 
gil casares
 
157
provincial
 
12
Other values (11)
 
24

Length

Max length20
Median length7
Mean length5.4460503
Min length2

Characters and Unicode

Total characters21855
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.2%

Sample

1st rowgil casares
2nd rowclinico
3rd rowclinico
4th rowclinico
5th rowclinico

Common Values

ValueCountFrequency (%)
clinico 2240
55.8%
no 1251
31.2%
conxo 329
 
8.2%
gil casares 157
 
3.9%
provincial 12
 
0.3%
chuac 6
 
0.1%
residencia 5
 
0.1%
si 3
 
0.1%
rosaleda 3
 
0.1%
hula 1
 
< 0.1%
Other values (6) 6
 
0.1%

Length

2023-06-29T16:03:15.289392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clinico 2240
53.6%
no 1251
30.0%
conxo 329
 
7.9%
gil 157
 
3.8%
casares 157
 
3.8%
provincial 12
 
0.3%
chuac 6
 
0.1%
residencia 5
 
0.1%
si 3
 
0.1%
rosaleda 3
 
0.1%
Other values (11) 13
 
0.3%

Most occurring characters

ValueCountFrequency (%)
c 4998
22.9%
i 4676
21.4%
o 4168
19.1%
n 3843
17.6%
l 2418
11.1%
a 355
 
1.6%
s 330
 
1.5%
x 329
 
1.5%
r 182
 
0.8%
e 174
 
0.8%
Other values (10) 382
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21692
99.3%
Space Separator 163
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4998
23.0%
i 4676
21.6%
o 4168
19.2%
n 3843
17.7%
l 2418
11.1%
a 355
 
1.6%
s 330
 
1.5%
x 329
 
1.5%
r 182
 
0.8%
e 174
 
0.8%
Other values (9) 219
 
1.0%
Space Separator
ValueCountFrequency (%)
163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21692
99.3%
Common 163
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4998
23.0%
i 4676
21.6%
o 4168
19.2%
n 3843
17.7%
l 2418
11.1%
a 355
 
1.6%
s 330
 
1.5%
x 329
 
1.5%
r 182
 
0.8%
e 174
 
0.8%
Other values (9) 219
 
1.0%
Common
ValueCountFrequency (%)
163
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4998
22.9%
i 4676
21.4%
o 4168
19.1%
n 3843
17.6%
l 2418
11.1%
a 355
 
1.6%
s 330
 
1.5%
x 329
 
1.5%
r 182
 
0.8%
e 174
 
0.8%
Other values (10) 382
 
1.7%
Distinct99
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:15.498399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length29
Mean length6.0134563
Min length2

Characters and Unicode

Total characters24132
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)1.0%

Sample

1st rowupal
2nd rowoncologia
3rd rowmir
4th rowdigestivo
5th rowurgencias
ValueCountFrequency (%)
no 1226
29.0%
oncologia 567
13.4%
mir 536
12.7%
urgencias 439
 
10.4%
upal 208
 
4.9%
neumologia 185
 
4.4%
digestivo 144
 
3.4%
acv 116
 
2.7%
hematologia 101
 
2.4%
cirugia 65
 
1.5%
Other values (77) 637
15.1%
2023-06-29T16:03:15.857694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4228
17.5%
i 2763
11.4%
n 2709
11.2%
a 2464
10.2%
g 1799
7.5%
c 1526
 
6.3%
r 1479
 
6.1%
l 1422
 
5.9%
e 1128
 
4.7%
u 1123
 
4.7%
Other values (13) 3491
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23921
99.1%
Space Separator 211
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4228
17.7%
i 2763
11.6%
n 2709
11.3%
a 2464
10.3%
g 1799
7.5%
c 1526
 
6.4%
r 1479
 
6.2%
l 1422
 
5.9%
e 1128
 
4.7%
u 1123
 
4.7%
Other values (12) 3280
13.7%
Space Separator
ValueCountFrequency (%)
211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23921
99.1%
Common 211
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4228
17.7%
i 2763
11.6%
n 2709
11.3%
a 2464
10.3%
g 1799
7.5%
c 1526
 
6.4%
r 1479
 
6.2%
l 1422
 
5.9%
e 1128
 
4.7%
u 1123
 
4.7%
Other values (12) 3280
13.7%
Common
ValueCountFrequency (%)
211
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24131
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4228
17.5%
i 2763
11.5%
n 2709
11.2%
a 2464
10.2%
g 1799
7.5%
c 1526
 
6.3%
r 1479
 
6.1%
l 1422
 
5.9%
e 1128
 
4.7%
u 1123
 
4.7%
Other values (12) 3490
14.5%
None
ValueCountFrequency (%)
í 1
100.0%

ap
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
no
2767 
si
1235 
ap
 
5
residencia
 
2
pac
 
1
Other values (2)
 
2

Length

Max length16
Median length2
Mean length2.0079761
Min length2

Characters and Unicode

Total characters8056
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2767
69.0%
si 1235
30.8%
ap 5
 
0.1%
residencia 2
 
< 0.1%
pac 1
 
< 0.1%
consulta externa 1
 
< 0.1%
uro 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-06-29T16:03:16.015692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:16.188045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2767
69.0%
si 1235
30.8%
ap 5
 
0.1%
residencia 2
 
< 0.1%
pac 1
 
< 0.1%
consulta 1
 
< 0.1%
externa 1
 
< 0.1%
uro 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2771
34.4%
o 2769
34.4%
i 1239
15.4%
s 1238
15.4%
a 10
 
0.1%
p 6
 
0.1%
e 6
 
0.1%
r 4
 
< 0.1%
c 4
 
< 0.1%
d 2
 
< 0.1%
Other values (5) 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8055
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2771
34.4%
o 2769
34.4%
i 1239
15.4%
s 1238
15.4%
a 10
 
0.1%
p 6
 
0.1%
e 6
 
0.1%
r 4
 
< 0.1%
c 4
 
< 0.1%
d 2
 
< 0.1%
Other values (4) 6
 
0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8055
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2771
34.4%
o 2769
34.4%
i 1239
15.4%
s 1238
15.4%
a 10
 
0.1%
p 6
 
0.1%
e 6
 
0.1%
r 4
 
< 0.1%
c 4
 
< 0.1%
d 2
 
< 0.1%
Other values (4) 6
 
0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2771
34.4%
o 2769
34.4%
i 1239
15.4%
s 1238
15.4%
a 10
 
0.1%
p 6
 
0.1%
e 6
 
0.1%
r 4
 
< 0.1%
c 4
 
< 0.1%
d 2
 
< 0.1%
Other values (5) 7
 
0.1%

otros
Categorical

IMBALANCE  MISSING 

Distinct49
Distinct (%)1.7%
Missing1055
Missing (%)26.3%
Memory size191.7 KiB
no
2811 
ncr
 
46
nef
 
20
onc
 
11
residencia
 
8
Other values (44)
 
62

Length

Max length34
Median length2
Mean length2.199121
Min length2

Characters and Unicode

Total characters6505
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)1.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2811
70.0%
ncr 46
 
1.1%
nef 20
 
0.5%
onc 11
 
0.3%
residencia 8
 
0.2%
urg 7
 
0.2%
residencia sanitaria 3
 
0.1%
nefro 3
 
0.1%
familia 3
 
0.1%
cgd 2
 
< 0.1%
Other values (39) 44
 
1.1%
(Missing) 1055
 
26.3%

Length

2023-06-29T16:03:16.352754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 2811
94.0%
ncr 46
 
1.5%
nef 20
 
0.7%
residencia 14
 
0.5%
onc 11
 
0.4%
urg 7
 
0.2%
nefro 5
 
0.2%
rosaleda 5
 
0.2%
dig 4
 
0.1%
cardiologia 3
 
0.1%
Other values (48) 64
 
2.1%

Most occurring characters

ValueCountFrequency (%)
n 2927
45.0%
o 2860
44.0%
r 103
 
1.6%
c 99
 
1.5%
a 89
 
1.4%
i 84
 
1.3%
e 68
 
1.0%
s 48
 
0.7%
d 37
 
0.6%
l 35
 
0.5%
Other values (14) 155
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6470
99.5%
Space Separator 32
 
0.5%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2927
45.2%
o 2860
44.2%
r 103
 
1.6%
c 99
 
1.5%
a 89
 
1.4%
i 84
 
1.3%
e 68
 
1.1%
s 48
 
0.7%
d 37
 
0.6%
l 35
 
0.5%
Other values (10) 120
 
1.9%
Decimal Number
ValueCountFrequency (%)
6 1
33.3%
1 1
33.3%
0 1
33.3%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6470
99.5%
Common 35
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2927
45.2%
o 2860
44.2%
r 103
 
1.6%
c 99
 
1.5%
a 89
 
1.4%
i 84
 
1.3%
e 68
 
1.1%
s 48
 
0.7%
d 37
 
0.6%
l 35
 
0.5%
Other values (10) 120
 
1.9%
Common
ValueCountFrequency (%)
32
91.4%
6 1
 
2.9%
1 1
 
2.9%
0 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2927
45.0%
o 2860
44.0%
r 103
 
1.6%
c 99
 
1.5%
a 89
 
1.4%
i 84
 
1.3%
e 68
 
1.0%
s 48
 
0.7%
d 37
 
0.6%
l 35
 
0.5%
Other values (14) 155
 
2.4%
Distinct1518
Distinct (%)37.9%
Missing3
Missing (%)0.1%
Memory size191.7 KiB
2023-06-29T16:03:16.610764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length220
Median length97
Mean length22.181546
Min length3

Characters and Unicode

Total characters88948
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1156 ?
Unique (%)28.8%

Sample

1st rowcancer orl
2nd rowmelanoma vulvar
3rd rowcancer broncogenico
4th rowcirrosis hepatica
5th rowneplasia de mama estadio IV
ValueCountFrequency (%)
cancer 806
 
7.3%
infeccion 609
 
5.5%
de 547
 
4.9%
iv 400
 
3.6%
estadio 386
 
3.5%
insuficiencia 243
 
2.2%
del 222
 
2.0%
urinario 221
 
2.0%
tracto 221
 
2.0%
pulmon 221
 
2.0%
Other values (1344) 7216
65.1%
2023-06-29T16:03:17.108756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10284
11.6%
i 9963
11.2%
c 7834
8.8%
e 7713
8.7%
7094
 
8.0%
o 7056
 
7.9%
n 6608
 
7.4%
r 6378
 
7.2%
s 3830
 
4.3%
t 3573
 
4.0%
Other values (31) 18615
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79909
89.8%
Space Separator 7094
 
8.0%
Uppercase Letter 1457
 
1.6%
Close Punctuation 220
 
0.2%
Open Punctuation 220
 
0.2%
Decimal Number 48
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10284
12.9%
i 9963
12.5%
c 7834
9.8%
e 7713
9.7%
o 7056
8.8%
n 6608
8.3%
r 6378
8.0%
s 3830
 
4.8%
t 3573
 
4.5%
d 2942
 
3.7%
Other values (16) 13728
17.2%
Decimal Number
ValueCountFrequency (%)
7 13
27.1%
2 10
20.8%
1 10
20.8%
4 5
 
10.4%
5 4
 
8.3%
0 3
 
6.2%
3 2
 
4.2%
6 1
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
I 619
42.5%
V 400
27.5%
T 219
 
15.0%
U 219
 
15.0%
Space Separator
ValueCountFrequency (%)
7094
100.0%
Close Punctuation
ValueCountFrequency (%)
) 220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81366
91.5%
Common 7582
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10284
12.6%
i 9963
12.2%
c 7834
9.6%
e 7713
9.5%
o 7056
8.7%
n 6608
8.1%
r 6378
7.8%
s 3830
 
4.7%
t 3573
 
4.4%
d 2942
 
3.6%
Other values (20) 15185
18.7%
Common
ValueCountFrequency (%)
7094
93.6%
) 220
 
2.9%
( 220
 
2.9%
7 13
 
0.2%
2 10
 
0.1%
1 10
 
0.1%
4 5
 
0.1%
5 4
 
0.1%
0 3
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10284
11.6%
i 9963
11.2%
c 7834
8.8%
e 7713
8.7%
7094
 
8.0%
o 7056
 
7.9%
n 6608
 
7.4%
r 6378
 
7.2%
s 3830
 
4.3%
t 3573
 
4.0%
Other values (31) 18615
20.9%
Distinct582
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:17.342766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length117
Median length66
Mean length19.284077
Min length3

Characters and Unicode

Total characters77387
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)10.0%

Sample

1st rowcontrol de sintomas
2nd rowmal control dolor
3rd rowcontrol evolutivo
4th rowadmininistacion octreotido
5th rowcontrol de sintomas
ValueCountFrequency (%)
control 1591
16.0%
sintomas 1309
13.1%
de 1284
12.9%
iv 930
 
9.3%
tratamiento 741
 
7.4%
antibiotico 509
 
5.1%
antibioterapia 419
 
4.2%
cuidados 238
 
2.4%
valoracion 218
 
2.2%
y 202
 
2.0%
Other values (451) 2525
25.3%
2023-06-29T16:03:17.771814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 9779
12.6%
t 8442
10.9%
i 8057
10.4%
a 7669
9.9%
n 6519
8.4%
5953
7.7%
r 4325
 
5.6%
s 4214
 
5.4%
e 4029
 
5.2%
c 3941
 
5.1%
Other values (23) 14459
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69566
89.9%
Space Separator 5953
 
7.7%
Uppercase Letter 1860
 
2.4%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9779
14.1%
t 8442
12.1%
i 8057
11.6%
a 7669
11.0%
n 6519
9.4%
r 4325
6.2%
s 4214
6.1%
e 4029
5.8%
c 3941
 
5.7%
l 2651
 
3.8%
Other values (15) 9940
14.3%
Decimal Number
ValueCountFrequency (%)
5 4
50.0%
0 1
 
12.5%
3 1
 
12.5%
1 1
 
12.5%
2 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
I 930
50.0%
V 930
50.0%
Space Separator
ValueCountFrequency (%)
5953
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71426
92.3%
Common 5961
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9779
13.7%
t 8442
11.8%
i 8057
11.3%
a 7669
10.7%
n 6519
9.1%
r 4325
 
6.1%
s 4214
 
5.9%
e 4029
 
5.6%
c 3941
 
5.5%
l 2651
 
3.7%
Other values (17) 11800
16.5%
Common
ValueCountFrequency (%)
5953
99.9%
5 4
 
0.1%
0 1
 
< 0.1%
3 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9779
12.6%
t 8442
10.9%
i 8057
10.4%
a 7669
9.9%
n 6519
8.4%
5953
7.7%
r 4325
 
5.6%
s 4214
 
5.4%
e 4029
 
5.2%
c 3941
 
5.1%
Other values (23) 14459
18.7%

paliativo_onc_noc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
no
2487 
si
1523 
so
 
1
m
 
1

Length

Max length2
Median length2
Mean length1.9997507
Min length1

Characters and Unicode

Total characters8023
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
no 2487
62.0%
si 1523
38.0%
so 1
 
< 0.1%
m 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-06-29T16:03:17.932803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:18.085813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2487
62.0%
si 1523
38.0%
so 1
 
< 0.1%
m 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 2488
31.0%
n 2487
31.0%
s 1524
19.0%
i 1523
19.0%
m 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8023
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2488
31.0%
n 2487
31.0%
s 1524
19.0%
i 1523
19.0%
m 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8023
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2488
31.0%
n 2487
31.0%
s 1524
19.0%
i 1523
19.0%
m 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2488
31.0%
n 2487
31.0%
s 1524
19.0%
i 1523
19.0%
m 1
 
< 0.1%

paliativo_no_onc_noc
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size191.7 KiB
no
2888 
si
1122 
m
 
1

Length

Max length2
Median length2
Mean length1.9997507
Min length1

Characters and Unicode

Total characters8021
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsi
2nd rowno
3rd rowsi
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2888
72.0%
si 1122
 
28.0%
m 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2023-06-29T16:03:18.224802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:18.380799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2888
72.0%
si 1122
 
28.0%
m 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2888
36.0%
o 2888
36.0%
s 1122
 
14.0%
i 1122
 
14.0%
m 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8021
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2888
36.0%
o 2888
36.0%
s 1122
 
14.0%
i 1122
 
14.0%
m 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2888
36.0%
o 2888
36.0%
s 1122
 
14.0%
i 1122
 
14.0%
m 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2888
36.0%
o 2888
36.0%
s 1122
 
14.0%
i 1122
 
14.0%
m 1
 
< 0.1%

fiebre
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size191.7 KiB
no
3476 
si
530 
ni
 
4
b
 
1

Length

Max length2
Median length2
Mean length1.9997507
Min length1

Characters and Unicode

Total characters8021
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 3476
86.6%
si 530
 
13.2%
ni 4
 
0.1%
b 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2023-06-29T16:03:18.508793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:18.660137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 3476
86.7%
si 530
 
13.2%
ni 4
 
0.1%
b 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 3480
43.4%
o 3476
43.3%
i 534
 
6.7%
s 530
 
6.6%
b 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8021
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3480
43.4%
o 3476
43.3%
i 534
 
6.7%
s 530
 
6.6%
b 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3480
43.4%
o 3476
43.3%
i 534
 
6.7%
s 530
 
6.6%
b 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3480
43.4%
o 3476
43.3%
i 534
 
6.7%
s 530
 
6.6%
b 1
 
< 0.1%

disnea
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
no
2824 
si
1188 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8024
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowsi
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
no 2824
70.4%
si 1188
29.6%
(Missing) 1
 
< 0.1%

Length

2023-06-29T16:03:18.788147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:18.927495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2824
70.4%
si 1188
29.6%

Most occurring characters

ValueCountFrequency (%)
n 2824
35.2%
o 2824
35.2%
s 1188
14.8%
i 1188
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8024
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2824
35.2%
o 2824
35.2%
s 1188
14.8%
i 1188
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 8024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2824
35.2%
o 2824
35.2%
s 1188
14.8%
i 1188
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2824
35.2%
o 2824
35.2%
s 1188
14.8%
i 1188
14.8%

dolor
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
no
2558 
si
1454 
ni
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8026
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsi
2nd rowsi
3rd rowno
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
no 2558
63.7%
si 1454
36.2%
ni 1
 
< 0.1%

Length

2023-06-29T16:03:19.047497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:19.191222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2558
63.7%
si 1454
36.2%
ni 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2559
31.9%
o 2558
31.9%
i 1455
18.1%
s 1454
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8026
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2559
31.9%
o 2558
31.9%
i 1455
18.1%
s 1454
18.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8026
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2559
31.9%
o 2558
31.9%
i 1455
18.1%
s 1454
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2559
31.9%
o 2558
31.9%
i 1455
18.1%
s 1454
18.1%

delirium
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
no
3351 
si
661 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8024
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 3351
83.5%
si 661
 
16.5%
(Missing) 1
 
< 0.1%

Length

2023-06-29T16:03:19.315207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:19.456204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 3351
83.5%
si 661
 
16.5%

Most occurring characters

ValueCountFrequency (%)
n 3351
41.8%
o 3351
41.8%
s 661
 
8.2%
i 661
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8024
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3351
41.8%
o 3351
41.8%
s 661
 
8.2%
i 661
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3351
41.8%
o 3351
41.8%
s 661
 
8.2%
i 661
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3351
41.8%
o 3351
41.8%
s 661
 
8.2%
i 661
 
8.2%

otros_1
Text

MISSING 

Distinct174
Distinct (%)11.2%
Missing2464
Missing (%)61.4%
Memory size191.7 KiB
2023-06-29T16:03:19.980248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length41
Median length2
Mean length4.5100065
Min length2

Characters and Unicode

Total characters6986
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)7.9%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 1197
65.4%
vomitos 51
 
2.8%
nauseas 42
 
2.3%
y 34
 
1.9%
n 28
 
1.5%
si 17
 
0.9%
agitacion 17
 
0.9%
insomnio 16
 
0.9%
de 16
 
0.9%
diarrea 15
 
0.8%
Other values (198) 397
 
21.7%
2023-06-29T16:03:20.387830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1600
22.9%
n 1564
22.4%
i 568
 
8.1%
a 521
 
7.5%
s 408
 
5.8%
e 403
 
5.8%
282
 
4.0%
r 241
 
3.4%
t 232
 
3.3%
c 201
 
2.9%
Other values (17) 966
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6703
95.9%
Space Separator 282
 
4.0%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1600
23.9%
n 1564
23.3%
i 568
 
8.5%
a 521
 
7.8%
s 408
 
6.1%
e 403
 
6.0%
r 241
 
3.6%
t 232
 
3.5%
c 201
 
3.0%
d 170
 
2.5%
Other values (15) 795
11.9%
Space Separator
ValueCountFrequency (%)
282
100.0%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6703
95.9%
Common 283
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1600
23.9%
n 1564
23.3%
i 568
 
8.5%
a 521
 
7.8%
s 408
 
6.1%
e 403
 
6.0%
r 241
 
3.6%
t 232
 
3.5%
c 201
 
3.0%
d 170
 
2.5%
Other values (15) 795
11.9%
Common
ValueCountFrequency (%)
282
99.6%
0 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1600
22.9%
n 1564
22.4%
i 568
 
8.1%
a 521
 
7.5%
s 408
 
5.8%
e 403
 
5.8%
282
 
4.0%
r 241
 
3.4%
t 232
 
3.3%
c 201
 
2.9%
Other values (17) 966
13.8%

p_terminal
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.2%
Missing2466
Missing (%)61.5%
Memory size191.7 KiB
no
776 
si
766 
nn
 
5

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3094
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowno
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
no 776
 
19.3%
si 766
 
19.1%
nn 5
 
0.1%
(Missing) 2466
61.5%

Length

2023-06-29T16:03:20.545231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:20.693356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 776
50.2%
si 766
49.5%
nn 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 786
25.4%
o 776
25.1%
s 766
24.8%
i 766
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3094
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 786
25.4%
o 776
25.1%
s 766
24.8%
i 766
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3094
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 786
25.4%
o 776
25.1%
s 766
24.8%
i 766
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 786
25.4%
o 776
25.1%
s 766
24.8%
i 766
24.8%

agonia
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing1058
Missing (%)26.4%
Memory size191.7 KiB
no
2450 
si
505 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5910
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2450
61.1%
si 505
 
12.6%
(Missing) 1058
26.4%

Length

2023-06-29T16:03:20.823601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:20.963637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2450
82.9%
si 505
 
17.1%

Most occurring characters

ValueCountFrequency (%)
n 2450
41.5%
o 2450
41.5%
s 505
 
8.5%
i 505
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5910
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2450
41.5%
o 2450
41.5%
s 505
 
8.5%
i 505
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5910
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2450
41.5%
o 2450
41.5%
s 505
 
8.5%
i 505
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2450
41.5%
o 2450
41.5%
s 505
 
8.5%
i 505
 
8.5%

ps_ecog
Categorical

Distinct15
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
no
2011 
40
963 
34
408 
23
211 
12
 
162
Other values (10)
257 

Length

Max length4
Median length2
Mean length2.001994
Min length2

Characters and Unicode

Total characters8032
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row40
2nd row34
3rd row34
4th row10
5th row40

Common Values

ValueCountFrequency (%)
no 2011
50.1%
40 963
24.0%
34 408
 
10.2%
23 211
 
5.3%
12 162
 
4.0%
30 93
 
2.3%
10 69
 
1.7%
20 54
 
1.3%
nk 21
 
0.5%
00 9
 
0.2%
Other values (5) 11
 
0.3%

Length

2023-06-29T16:03:21.087886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 2011
50.1%
40 963
24.0%
34 408
 
10.2%
23 211
 
5.3%
12 162
 
4.0%
30 93
 
2.3%
10 69
 
1.7%
20 54
 
1.3%
nk 21
 
0.5%
00 9
 
0.2%
Other values (5) 11
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 2034
25.3%
o 2011
25.0%
4 1375
17.1%
0 1206
15.0%
3 714
 
8.9%
2 429
 
5.3%
1 236
 
2.9%
k 21
 
0.3%
s 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4070
50.7%
Decimal Number 3962
49.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1375
34.7%
0 1206
30.4%
3 714
18.0%
2 429
 
10.8%
1 236
 
6.0%
7 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n 2034
50.0%
o 2011
49.4%
k 21
 
0.5%
s 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4070
50.7%
Common 3962
49.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1375
34.7%
0 1206
30.4%
3 714
18.0%
2 429
 
10.8%
1 236
 
6.0%
7 2
 
0.1%
Latin
ValueCountFrequency (%)
n 2034
50.0%
o 2011
49.4%
k 21
 
0.5%
s 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2034
25.3%
o 2011
25.0%
4 1375
17.1%
0 1206
15.0%
3 714
 
8.9%
2 429
 
5.3%
1 236
 
2.9%
k 21
 
0.3%
s 2
 
< 0.1%
i 2
 
< 0.1%

barthel
Categorical

Distinct41
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
20
1008 
no
394 
10
339 
1000
330 
40
195 
Other values (36)
1746 

Length

Max length4
Median length2
Mean length2.4952642
Min length1

Characters and Unicode

Total characters10011
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.2%

Sample

1st row300
2nd rowno
3rd row400
4th row900
5th row300

Common Values

ValueCountFrequency (%)
20 1008
25.1%
no 394
 
9.8%
10 339
 
8.4%
1000 330
 
8.2%
40 195
 
4.9%
800 190
 
4.7%
400 189
 
4.7%
600 184
 
4.6%
900 178
 
4.4%
30 157
 
3.9%
Other values (31) 848
21.1%

Length

2023-06-29T16:03:21.240187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20 1008
25.1%
no 394
 
9.8%
10 339
 
8.4%
1000 330
 
8.2%
40 195
 
4.9%
800 190
 
4.7%
400 189
 
4.7%
600 184
 
4.6%
900 178
 
4.4%
30 157
 
3.9%
Other values (31) 848
21.1%

Most occurring characters

ValueCountFrequency (%)
0 5512
55.1%
2 1071
 
10.7%
1 688
 
6.9%
4 448
 
4.5%
n 398
 
4.0%
o 394
 
3.9%
3 338
 
3.4%
6 336
 
3.4%
9 276
 
2.8%
8 232
 
2.3%
Other values (3) 318
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9218
92.1%
Lowercase Letter 793
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5512
59.8%
2 1071
 
11.6%
1 688
 
7.5%
4 448
 
4.9%
3 338
 
3.7%
6 336
 
3.6%
9 276
 
3.0%
8 232
 
2.5%
5 200
 
2.2%
7 117
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
n 398
50.2%
o 394
49.7%
z 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9218
92.1%
Latin 793
 
7.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5512
59.8%
2 1071
 
11.6%
1 688
 
7.5%
4 448
 
4.9%
3 338
 
3.7%
6 336
 
3.6%
9 276
 
3.0%
8 232
 
2.5%
5 200
 
2.2%
7 117
 
1.3%
Latin
ValueCountFrequency (%)
n 398
50.2%
o 394
49.7%
z 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5512
55.1%
2 1071
 
10.7%
1 688
 
6.9%
4 448
 
4.5%
n 398
 
4.0%
o 394
 
3.9%
3 338
 
3.4%
6 336
 
3.4%
9 276
 
2.8%
8 232
 
2.3%
Other values (3) 318
 
3.2%

gds_fast
Categorical

IMBALANCE 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
no
3170 
7c
 
242
70
 
148
60
 
94
50
 
85
Other values (16)
 
274

Length

Max length2
Median length2
Mean length1.9985049
Min length1

Characters and Unicode

Total characters8020
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 3170
79.0%
7c 242
 
6.0%
70 148
 
3.7%
60 94
 
2.3%
50 85
 
2.1%
40 63
 
1.6%
30 63
 
1.6%
20 39
 
1.0%
45 38
 
0.9%
67 31
 
0.8%
Other values (11) 40
 
1.0%

Length

2023-06-29T16:03:21.390457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 3170
79.0%
7c 242
 
6.0%
70 148
 
3.7%
60 94
 
2.3%
50 85
 
2.1%
40 63
 
1.6%
30 63
 
1.6%
20 39
 
1.0%
45 38
 
0.9%
67 31
 
0.8%
Other values (11) 40
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 3174
39.6%
o 3170
39.5%
0 498
 
6.2%
7 436
 
5.4%
c 242
 
3.0%
5 131
 
1.6%
6 129
 
1.6%
4 108
 
1.3%
3 68
 
0.8%
2 39
 
0.5%
Other values (5) 25
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6605
82.4%
Decimal Number 1415
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 498
35.2%
7 436
30.8%
5 131
 
9.3%
6 129
 
9.1%
4 108
 
7.6%
3 68
 
4.8%
2 39
 
2.8%
1 6
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n 3174
48.1%
o 3170
48.0%
c 242
 
3.7%
a 14
 
0.2%
s 2
 
< 0.1%
i 2
 
< 0.1%
b 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6605
82.4%
Common 1415
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 498
35.2%
7 436
30.8%
5 131
 
9.3%
6 129
 
9.1%
4 108
 
7.6%
3 68
 
4.8%
2 39
 
2.8%
1 6
 
0.4%
Latin
ValueCountFrequency (%)
n 3174
48.1%
o 3170
48.0%
c 242
 
3.7%
a 14
 
0.2%
s 2
 
< 0.1%
i 2
 
< 0.1%
b 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3174
39.6%
o 3170
39.5%
0 498
 
6.2%
7 436
 
5.4%
c 242
 
3.0%
5 131
 
1.6%
6 129
 
1.6%
4 108
 
1.3%
3 68
 
0.8%
2 39
 
0.5%
Other values (5) 25
 
0.3%

eva_ing
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)0.8%
Missing2465
Missing (%)61.4%
Memory size191.7 KiB
no
1402 
80
 
38
70
 
28
60
 
24
50
 
19
Other values (8)
 
37

Length

Max length3
Median length2
Mean length2.001292
Min length2

Characters and Unicode

Total characters3098
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th row70

Common Values

ValueCountFrequency (%)
no 1402
34.9%
80 38
 
0.9%
70 28
 
0.7%
60 24
 
0.6%
50 19
 
0.5%
90 13
 
0.3%
30 9
 
0.2%
40 8
 
0.2%
100 2
 
< 0.1%
20 2
 
< 0.1%
Other values (3) 3
 
0.1%
(Missing) 2465
61.4%

Length

2023-06-29T16:03:21.533777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 1402
90.6%
80 38
 
2.5%
70 28
 
1.8%
60 24
 
1.6%
50 19
 
1.2%
90 13
 
0.8%
30 9
 
0.6%
40 8
 
0.5%
100 2
 
0.1%
20 2
 
0.1%
Other values (3) 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 1402
45.3%
o 1402
45.3%
0 147
 
4.7%
8 39
 
1.3%
7 29
 
0.9%
6 24
 
0.8%
5 19
 
0.6%
9 13
 
0.4%
3 10
 
0.3%
4 8
 
0.3%
Other values (2) 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2804
90.5%
Decimal Number 294
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
50.0%
8 39
 
13.3%
7 29
 
9.9%
6 24
 
8.2%
5 19
 
6.5%
9 13
 
4.4%
3 10
 
3.4%
4 8
 
2.7%
2 3
 
1.0%
1 2
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
n 1402
50.0%
o 1402
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2804
90.5%
Common 294
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
50.0%
8 39
 
13.3%
7 29
 
9.9%
6 24
 
8.2%
5 19
 
6.5%
9 13
 
4.4%
3 10
 
3.4%
4 8
 
2.7%
2 3
 
1.0%
1 2
 
0.7%
Latin
ValueCountFrequency (%)
n 1402
50.0%
o 1402
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1402
45.3%
o 1402
45.3%
0 147
 
4.7%
8 39
 
1.3%
7 29
 
0.9%
6 24
 
0.8%
5 19
 
0.6%
9 13
 
0.4%
3 10
 
0.3%
4 8
 
0.3%
Other values (2) 5
 
0.2%

otros_2
Text

MISSING 

Distinct51
Distinct (%)3.3%
Missing2465
Missing (%)61.4%
Memory size191.7 KiB
2023-06-29T16:03:21.751041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length65
Median length2
Mean length2.4521964
Min length2

Characters and Unicode

Total characters3796
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)2.7%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 1484
92.1%
pap 9
 
0.6%
score 9
 
0.6%
c 7
 
0.4%
de 6
 
0.4%
ingesta 4
 
0.2%
negativa 4
 
0.2%
a 3
 
0.2%
diarrea 3
 
0.2%
ansiedad 2
 
0.1%
Other values (73) 80
 
5.0%
2023-06-29T16:03:22.153414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1538
40.5%
n 1527
40.2%
a 103
 
2.7%
i 89
 
2.3%
e 77
 
2.0%
s 68
 
1.8%
63
 
1.7%
r 54
 
1.4%
c 52
 
1.4%
t 38
 
1.0%
Other values (17) 187
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3731
98.3%
Space Separator 63
 
1.7%
Decimal Number 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1538
41.2%
n 1527
40.9%
a 103
 
2.8%
i 89
 
2.4%
e 77
 
2.1%
s 68
 
1.8%
r 54
 
1.4%
c 52
 
1.4%
t 38
 
1.0%
p 33
 
0.9%
Other values (14) 152
 
4.1%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3731
98.3%
Common 65
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1538
41.2%
n 1527
40.9%
a 103
 
2.8%
i 89
 
2.4%
e 77
 
2.1%
s 68
 
1.8%
r 54
 
1.4%
c 52
 
1.4%
t 38
 
1.0%
p 33
 
0.9%
Other values (14) 152
 
4.1%
Common
ValueCountFrequency (%)
63
96.9%
3 1
 
1.5%
0 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1538
40.5%
n 1527
40.2%
a 103
 
2.7%
i 89
 
2.3%
e 77
 
2.0%
s 68
 
1.8%
63
 
1.7%
r 54
 
1.4%
c 52
 
1.4%
t 38
 
1.0%
Other values (17) 187
 
4.9%

otros_complicaciones
Text

MISSING 

Distinct414
Distinct (%)14.0%
Missing1057
Missing (%)26.3%
Memory size191.7 KiB
2023-06-29T16:03:22.424559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length139
Median length2
Mean length5.4536536
Min length2

Characters and Unicode

Total characters16121
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique343 ?
Unique (%)11.6%

Sample

1st rowreingreso en urgencias
2nd rowmet pulmonares y digest
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 2380
63.2%
si 58
 
1.5%
de 32
 
0.8%
familiar 31
 
0.8%
infeccion 31
 
0.8%
respiratoria 28
 
0.7%
claudicacion 27
 
0.7%
urinaria 23
 
0.6%
dolor 19
 
0.5%
intestinal 18
 
0.5%
Other values (550) 1119
29.7%
2023-06-29T16:03:22.911294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3267
20.3%
n 3128
19.4%
i 1515
9.4%
a 1274
 
7.9%
e 922
 
5.7%
811
 
5.0%
r 808
 
5.0%
s 796
 
4.9%
c 706
 
4.4%
t 472
 
2.9%
Other values (17) 2422
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15307
95.0%
Space Separator 811
 
5.0%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3267
21.3%
n 3128
20.4%
i 1515
9.9%
a 1274
 
8.3%
e 922
 
6.0%
r 808
 
5.3%
s 796
 
5.2%
c 706
 
4.6%
t 472
 
3.1%
l 440
 
2.9%
Other values (14) 1979
12.9%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15307
95.0%
Common 814
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3267
21.3%
n 3128
20.4%
i 1515
9.9%
a 1274
 
8.3%
e 922
 
6.0%
r 808
 
5.3%
s 796
 
5.2%
c 706
 
4.6%
t 472
 
3.1%
l 440
 
2.9%
Other values (14) 1979
12.9%
Common
ValueCountFrequency (%)
811
99.6%
1 2
 
0.2%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3267
20.3%
n 3128
19.4%
i 1515
9.4%
a 1274
 
7.9%
e 922
 
5.7%
811
 
5.0%
r 808
 
5.0%
s 796
 
4.9%
c 706
 
4.4%
t 472
 
2.9%
Other values (17) 2422
15.0%

n_estancias
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.08173
Minimum0
Maximum7122
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:23.092652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q140
median90
Q3180
95-th percentile510
Maximum7122
Range7122
Interquartile range (IQR)140

Descriptive statistics

Standard deviation350.10179
Coefficient of variation (CV)2.1600324
Kurtosis272.78821
Mean162.08173
Median Absolute Deviation (MAD)60
Skewness14.380042
Sum650434
Variance122571.27
MonotonicityNot monotonic
2023-06-29T16:03:23.264704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 455
 
11.3%
70 269
 
6.7%
10 231
 
5.8%
50 231
 
5.8%
80 226
 
5.6%
40 192
 
4.8%
30 183
 
4.6%
60 180
 
4.5%
140 150
 
3.7%
90 149
 
3.7%
Other values (119) 1747
43.5%
ValueCountFrequency (%)
0 9
 
0.2%
10 231
5.8%
20 455
11.3%
30 183
4.6%
40 192
4.8%
50 231
5.8%
60 180
 
4.5%
70 269
6.7%
80 226
5.6%
90 149
 
3.7%
ValueCountFrequency (%)
7122 7
0.2%
3040 1
 
< 0.1%
2040 1
 
< 0.1%
1880 1
 
< 0.1%
1800 1
 
< 0.1%
1710 1
 
< 0.1%
1660 1
 
< 0.1%
1610 1
 
< 0.1%
1540 1
 
< 0.1%
1500 1
 
< 0.1%

n_visitas
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.214304
Minimum0
Maximum990
Zeros28
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:23.393806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median40
Q370
95-th percentile190
Maximum990
Range990
Interquartile range (IQR)50

Descriptive statistics

Standard deviation69.573576
Coefficient of variation (CV)1.1554327
Kurtosis25.207805
Mean60.214304
Median Absolute Deviation (MAD)20
Skewness3.8592676
Sum241640
Variance4840.4825
MonotonicityNot monotonic
2023-06-29T16:03:23.540779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 619
15.4%
10 607
15.1%
30 542
13.5%
40 448
11.2%
50 368
9.2%
60 295
7.4%
70 213
 
5.3%
80 153
 
3.8%
100 104
 
2.6%
90 86
 
2.1%
Other values (48) 578
14.4%
ValueCountFrequency (%)
0 28
 
0.7%
10 607
15.1%
20 619
15.4%
30 542
13.5%
40 448
11.2%
50 368
9.2%
60 295
7.4%
70 213
 
5.3%
80 153
 
3.8%
90 86
 
2.1%
ValueCountFrequency (%)
990 1
< 0.1%
800 1
< 0.1%
760 1
< 0.1%
710 1
< 0.1%
680 1
< 0.1%
600 1
< 0.1%
560 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
500 2
< 0.1%

sedacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct34
Distinct (%)0.8%
Missing5
Missing (%)0.1%
Memory size191.7 KiB
no
3320 
si
612 
si buscapmidazmorf
 
17
si midazolam morfina buscapina
 
8
si midazmorfbuscap
 
6
Other values (29)
 
45

Length

Max length46
Median length2
Mean length2.4263972
Min length2

Characters and Unicode

Total characters9725
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.6%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 3320
82.7%
si 612
 
15.3%
si buscapmidazmorf 17
 
0.4%
si midazolam morfina buscapina 8
 
0.2%
si midazmorfbuscap 6
 
0.1%
si levomepromazina midazolam buscapina morfina 5
 
0.1%
simorfbuscapmidaz 5
 
0.1%
si buscap midazolammorfina 4
 
0.1%
si buscap midazolam morfina 4
 
0.1%
simidmorfbuscap 3
 
0.1%
Other values (24) 24
 
0.6%
(Missing) 5
 
0.1%

Length

2023-06-29T16:03:23.708506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 3320
80.4%
si 668
 
16.2%
midazolam 25
 
0.6%
morfina 25
 
0.6%
buscapina 19
 
0.5%
buscapmidazmorf 17
 
0.4%
buscap 10
 
0.2%
levomepromazina 7
 
0.2%
midazmorfbuscap 6
 
0.1%
simorfbuscapmidaz 5
 
0.1%
Other values (21) 28
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 3466
35.6%
n 3388
34.8%
i 829
 
8.5%
s 758
 
7.8%
a 250
 
2.6%
m 192
 
2.0%
122
 
1.3%
r 88
 
0.9%
p 85
 
0.9%
d 80
 
0.8%
Other values (12) 467
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9601
98.7%
Space Separator 122
 
1.3%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3466
36.1%
n 3388
35.3%
i 829
 
8.6%
s 758
 
7.9%
a 250
 
2.6%
m 192
 
2.0%
r 88
 
0.9%
p 85
 
0.9%
d 80
 
0.8%
z 75
 
0.8%
Other values (10) 390
 
4.1%
Space Separator
ValueCountFrequency (%)
122
100.0%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9601
98.7%
Common 124
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3466
36.1%
n 3388
35.3%
i 829
 
8.6%
s 758
 
7.9%
a 250
 
2.6%
m 192
 
2.0%
r 88
 
0.9%
p 85
 
0.9%
d 80
 
0.8%
z 75
 
0.8%
Other values (10) 390
 
4.1%
Common
ValueCountFrequency (%)
122
98.4%
0 2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3466
35.6%
n 3388
34.8%
i 829
 
8.5%
s 758
 
7.8%
a 250
 
2.6%
m 192
 
2.0%
122
 
1.3%
r 88
 
0.9%
p 85
 
0.9%
d 80
 
0.8%
Other values (12) 467
 
4.8%
Distinct99
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:23.879311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length99
Median length59
Mean length11.497882
Min length2

Characters and Unicode

Total characters46141
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)1.7%

Sample

1st rowreingreso
2nd rowexitus
3rd rowfin de cuidados
4th rowfin de cuidados
5th rowexitus
ValueCountFrequency (%)
cuidados 2381
29.2%
fin 2380
29.2%
exitus 1213
14.9%
de 1058
13.0%
a 209
 
2.6%
traslado 197
 
2.4%
upal 124
 
1.5%
reingreso 121
 
1.5%
urgencias 121
 
1.5%
en 64
 
0.8%
Other values (102) 289
 
3.5%
2023-06-29T16:03:24.236543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6437
14.0%
d 6070
13.2%
s 4185
9.1%
4144
9.0%
u 3874
8.4%
a 3430
7.4%
o 2930
6.4%
n 2824
6.1%
e 2821
6.1%
c 2608
 
5.7%
Other values (15) 6818
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41994
91.0%
Space Separator 4144
 
9.0%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6437
15.3%
d 6070
14.5%
s 4185
10.0%
u 3874
9.2%
a 3430
8.2%
o 2930
7.0%
n 2824
6.7%
e 2821
6.7%
c 2608
6.2%
f 2389
 
5.7%
Other values (11) 4426
10.5%
Decimal Number
ValueCountFrequency (%)
0 1
33.3%
6 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
4144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41994
91.0%
Common 4147
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6437
15.3%
d 6070
14.5%
s 4185
10.0%
u 3874
9.2%
a 3430
8.2%
o 2930
7.0%
n 2824
6.7%
e 2821
6.7%
c 2608
6.2%
f 2389
 
5.7%
Other values (11) 4426
10.5%
Common
ValueCountFrequency (%)
4144
99.9%
0 1
 
< 0.1%
6 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6437
14.0%
d 6070
13.2%
s 4185
9.1%
4144
9.0%
u 3874
8.4%
a 3430
7.4%
o 2930
6.4%
n 2824
6.1%
e 2821
6.1%
c 2608
 
5.7%
Other values (15) 6818
14.8%

medico
Categorical

Distinct16
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Memory size191.7 KiB
fernandez benito
797 
villar del castillo
727 
galego feal
567 
lopez renedo
473 
suarez
420 
Other values (11)
1028 

Length

Max length19
Median length13
Mean length12.31655
Min length3

Characters and Unicode

Total characters49414
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowvaldes
2nd rowgalego feal
3rd rowlopez renedo
4th rowlopez renedo
5th rowlopez renedo

Common Values

ValueCountFrequency (%)
fernandez benito 797
19.9%
villar del castillo 727
18.1%
galego feal 567
14.1%
lopez renedo 473
11.8%
suarez 420
10.5%
valdes 221
 
5.5%
novo 182
 
4.5%
gomez buela 143
 
3.6%
suarez prado 140
 
3.5%
valcarcel 105
 
2.6%
Other values (6) 237
 
5.9%

Length

2023-06-29T16:03:24.404165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fernandez 797
10.3%
benito 797
10.3%
villar 727
9.4%
del 727
9.4%
castillo 727
9.4%
galego 567
7.4%
feal 567
7.4%
suarez 560
7.3%
lopez 473
 
6.1%
renedo 473
 
6.1%
Other values (13) 1286
16.7%

Most occurring characters

ValueCountFrequency (%)
e 7098
14.4%
l 5903
11.9%
a 5018
10.2%
o 3929
 
8.0%
3689
 
7.5%
n 3220
 
6.5%
r 2872
 
5.8%
i 2408
 
4.9%
d 2358
 
4.8%
z 2117
 
4.3%
Other values (13) 10802
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45722
92.5%
Space Separator 3689
 
7.5%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7098
15.5%
l 5903
12.9%
a 5018
11.0%
o 3929
8.6%
n 3220
 
7.0%
r 2872
 
6.3%
i 2408
 
5.3%
d 2358
 
5.2%
z 2117
 
4.6%
s 1673
 
3.7%
Other values (10) 9126
20.0%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
3689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45722
92.5%
Common 3692
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7098
15.5%
l 5903
12.9%
a 5018
11.0%
o 3929
8.6%
n 3220
 
7.0%
r 2872
 
6.3%
i 2408
 
5.3%
d 2358
 
5.2%
z 2117
 
4.6%
s 1673
 
3.7%
Other values (10) 9126
20.0%
Common
ValueCountFrequency (%)
3689
99.9%
0 2
 
0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7098
14.4%
l 5903
11.9%
a 5018
10.2%
o 3929
 
8.0%
3689
 
7.5%
n 3220
 
6.5%
r 2872
 
5.8%
i 2408
 
4.9%
d 2358
 
4.8%
z 2117
 
4.3%
Other values (13) 10802
21.9%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.9041
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size191.7 KiB
2023-06-29T16:03:24.540271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32022
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7863211
Coefficient of variation (CV)0.00088435938
Kurtosis-1.2727855
Mean2019.9041
Median Absolute Deviation (MAD)2
Skewness-0.33559822
Sum8105875
Variance3.1909431
MonotonicityIncreasing
2023-06-29T16:03:24.665550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 1055
26.3%
2021 765
19.1%
2020 643
16.0%
2017 587
14.6%
2018 536
13.4%
2019 427
10.6%
ValueCountFrequency (%)
2017 587
14.6%
2018 536
13.4%
2019 427
10.6%
2020 643
16.0%
2021 765
19.1%
2022 1055
26.3%
ValueCountFrequency (%)
2022 1055
26.3%
2021 765
19.1%
2020 643
16.0%
2019 427
10.6%
2018 536
13.4%
2017 587
14.6%

ast_anorx
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
no
2453 
si
1560 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8026
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2453
61.1%
si 1560
38.9%

Length

2023-06-29T16:03:24.800256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:24.944514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2453
61.1%
si 1560
38.9%

Most occurring characters

ValueCountFrequency (%)
n 2453
30.6%
o 2453
30.6%
s 1560
19.4%
i 1560
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8026
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2453
30.6%
o 2453
30.6%
s 1560
19.4%
i 1560
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8026
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2453
30.6%
o 2453
30.6%
s 1560
19.4%
i 1560
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2453
30.6%
o 2453
30.6%
s 1560
19.4%
i 1560
19.4%

agudo_estable
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing1551
Missing (%)38.6%
Memory size191.7 KiB
no
2030 
si
432 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4924
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2030
50.6%
si 432
 
10.8%
(Missing) 1551
38.6%

Length

2023-06-29T16:03:25.069514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:25.202668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2030
82.5%
si 432
 
17.5%

Most occurring characters

ValueCountFrequency (%)
n 2030
41.2%
o 2030
41.2%
s 432
 
8.8%
i 432
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4924
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2030
41.2%
o 2030
41.2%
s 432
 
8.8%
i 432
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 4924
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2030
41.2%
o 2030
41.2%
s 432
 
8.8%
i 432
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2030
41.2%
o 2030
41.2%
s 432
 
8.8%
i 432
 
8.8%

cronico_reag
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing1550
Missing (%)38.6%
Memory size191.7 KiB
si
1414 
no
1048 
m
 
1

Length

Max length2
Median length2
Mean length1.999594
Min length1

Characters and Unicode

Total characters4925
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowsi
3rd rowsi
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
si 1414
35.2%
no 1048
26.1%
m 1
 
< 0.1%
(Missing) 1550
38.6%

Length

2023-06-29T16:03:25.318369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:25.461622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 1414
57.4%
no 1048
42.5%
m 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 1414
28.7%
i 1414
28.7%
n 1048
21.3%
o 1048
21.3%
m 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4925
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1414
28.7%
i 1414
28.7%
n 1048
21.3%
o 1048
21.3%
m 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4925
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1414
28.7%
i 1414
28.7%
n 1048
21.3%
o 1048
21.3%
m 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1414
28.7%
i 1414
28.7%
n 1048
21.3%
o 1048
21.3%
m 1
 
< 0.1%

transfusion
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)0.1%
Missing1550
Missing (%)38.6%
Memory size191.7 KiB
no
2400 
si
 
62
2 concentrados
 
1

Length

Max length14
Median length2
Mean length2.0048721
Min length2

Characters and Unicode

Total characters4938
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2400
59.8%
si 62
 
1.5%
2 concentrados 1
 
< 0.1%
(Missing) 1550
38.6%

Length

2023-06-29T16:03:25.584570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:25.725442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2400
97.4%
si 62
 
2.5%
2 1
 
< 0.1%
concentrados 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 2402
48.6%
o 2402
48.6%
s 63
 
1.3%
i 62
 
1.3%
c 2
 
< 0.1%
2 1
 
< 0.1%
1
 
< 0.1%
e 1
 
< 0.1%
t 1
 
< 0.1%
r 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4936
> 99.9%
Decimal Number 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2402
48.7%
o 2402
48.7%
s 63
 
1.3%
i 62
 
1.3%
c 2
 
< 0.1%
e 1
 
< 0.1%
t 1
 
< 0.1%
r 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4936
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2402
48.7%
o 2402
48.7%
s 63
 
1.3%
i 62
 
1.3%
c 2
 
< 0.1%
e 1
 
< 0.1%
t 1
 
< 0.1%
r 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
Common
ValueCountFrequency (%)
2 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2402
48.6%
o 2402
48.6%
s 63
 
1.3%
i 62
 
1.3%
c 2
 
< 0.1%
2 1
 
< 0.1%
1
 
< 0.1%
e 1
 
< 0.1%
t 1
 
< 0.1%
r 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

paracentesis
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing1550
Missing (%)38.6%
Memory size191.7 KiB
no
2392 
si
 
71

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4926
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 2392
59.6%
si 71
 
1.8%
(Missing) 1550
38.6%

Length

2023-06-29T16:03:25.840272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:25.970931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 2392
97.1%
si 71
 
2.9%

Most occurring characters

ValueCountFrequency (%)
n 2392
48.6%
o 2392
48.6%
s 71
 
1.4%
i 71
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4926
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2392
48.6%
o 2392
48.6%
s 71
 
1.4%
i 71
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4926
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2392
48.6%
o 2392
48.6%
s 71
 
1.4%
i 71
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2392
48.6%
o 2392
48.6%
s 71
 
1.4%
i 71
 
1.4%

toracocentesis
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing2606
Missing (%)64.9%
Memory size191.7 KiB
no
1404 
si
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2814
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1404
35.0%
si 3
 
0.1%
(Missing) 2606
64.9%

Length

2023-06-29T16:03:26.091254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:26.248284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1404
99.8%
si 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 1404
49.9%
o 1404
49.9%
s 3
 
0.1%
i 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2814
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1404
49.9%
o 1404
49.9%
s 3
 
0.1%
i 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2814
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1404
49.9%
o 1404
49.9%
s 3
 
0.1%
i 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1404
49.9%
o 1404
49.9%
s 3
 
0.1%
i 3
 
0.1%

ayuntamiento
Categorical

MISSING 

Distinct43
Distinct (%)1.7%
Missing1551
Missing (%)38.6%
Memory size191.7 KiB
santiago
983 
lalin
249 
ames
209 
teo
183 
la estrada
 
71
Other values (38)
767 

Length

Max length22
Median length19
Mean length6.7270512
Min length3

Characters and Unicode

Total characters16562
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.4%

Sample

1st rowsantiago
2nd rowsantiago
3rd rowsantiago
4th rowbrion
5th rowlalin

Common Values

ValueCountFrequency (%)
santiago 983
24.5%
lalin 249
 
6.2%
ames 209
 
5.2%
teo 183
 
4.6%
la estrada 71
 
1.8%
padron 63
 
1.6%
brion 57
 
1.4%
ordenes 45
 
1.1%
rois 44
 
1.1%
boqueixon 44
 
1.1%
Other values (33) 514
 
12.8%
(Missing) 1551
38.6%

Length

2023-06-29T16:03:26.385517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santiago 1009
37.1%
lalin 249
 
9.2%
ames 209
 
7.7%
teo 183
 
6.7%
estrada 114
 
4.2%
la 71
 
2.6%
padron 63
 
2.3%
brion 57
 
2.1%
a 52
 
1.9%
ordenes 45
 
1.7%
Other values (38) 667
24.5%

Most occurring characters

ValueCountFrequency (%)
a 3315
20.0%
o 1938
11.7%
n 1677
10.1%
s 1603
9.7%
i 1576
9.5%
t 1410
8.5%
g 1064
 
6.4%
e 944
 
5.7%
l 743
 
4.5%
r 629
 
3.8%
Other values (13) 1663
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16305
98.4%
Space Separator 257
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3315
20.3%
o 1938
11.9%
n 1677
10.3%
s 1603
9.8%
i 1576
9.7%
t 1410
8.6%
g 1064
 
6.5%
e 944
 
5.8%
l 743
 
4.6%
r 629
 
3.9%
Other values (12) 1406
8.6%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16305
98.4%
Common 257
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3315
20.3%
o 1938
11.9%
n 1677
10.3%
s 1603
9.8%
i 1576
9.7%
t 1410
8.6%
g 1064
 
6.5%
e 944
 
5.8%
l 743
 
4.6%
r 629
 
3.9%
Other values (12) 1406
8.6%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3315
20.0%
o 1938
11.7%
n 1677
10.1%
s 1603
9.7%
i 1576
9.5%
t 1410
8.5%
g 1064
 
6.4%
e 944
 
5.7%
l 743
 
4.5%
r 629
 
3.8%
Other values (13) 1663
10.0%

fecha_alta
Text

MISSING 

Distinct218
Distinct (%)28.5%
Missing3247
Missing (%)80.9%
Memory size191.7 KiB
2023-06-29T16:03:26.591932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.929504
Min length2

Characters and Unicode

Total characters11436
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)7.3%

Sample

1st row20220811t000000
2nd row20220603t000000
3rd row20220607t000000
4th row20220913t000000
5th row20220813t000000
ValueCountFrequency (%)
20220711t000000 10
 
1.3%
20220623t000000 10
 
1.3%
20220620t000000 9
 
1.2%
20220812t000000 9
 
1.2%
20221031t000000 9
 
1.2%
20221205t000000 8
 
1.0%
20220616t000000 8
 
1.0%
20221114t000000 8
 
1.0%
20221207t000000 8
 
1.0%
20221219t000000 8
 
1.0%
Other values (208) 679
88.6%
2023-06-29T16:03:26.953387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6171
54.0%
2 2725
23.8%
t 760
 
6.6%
1 753
 
6.6%
8 189
 
1.7%
7 174
 
1.5%
9 174
 
1.5%
6 169
 
1.5%
3 140
 
1.2%
5 105
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10676
93.4%
Lowercase Letter 760
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6171
57.8%
2 2725
25.5%
1 753
 
7.1%
8 189
 
1.8%
7 174
 
1.6%
9 174
 
1.6%
6 169
 
1.6%
3 140
 
1.3%
5 105
 
1.0%
4 76
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
t 760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10676
93.4%
Latin 760
 
6.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6171
57.8%
2 2725
25.5%
1 753
 
7.1%
8 189
 
1.8%
7 174
 
1.6%
9 174
 
1.6%
6 169
 
1.6%
3 140
 
1.3%
5 105
 
1.0%
4 76
 
0.7%
Latin
ValueCountFrequency (%)
t 760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6171
54.0%
2 2725
23.8%
t 760
 
6.6%
1 753
 
6.6%
8 189
 
1.7%
7 174
 
1.5%
9 174
 
1.5%
6 169
 
1.5%
3 140
 
1.2%
5 105
 
0.9%

¿?
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing2959
Missing (%)73.7%
Memory size191.7 KiB
no
1047 
si
 
7

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1047
 
26.1%
si 7
 
0.2%
(Missing) 2959
73.7%

Length

2023-06-29T16:03:27.118461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:27.252839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1047
99.3%
si 7
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 1047
49.7%
o 1047
49.7%
s 7
 
0.3%
i 7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2108
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1047
49.7%
o 1047
49.7%
s 7
 
0.3%
i 7
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1047
49.7%
o 1047
49.7%
s 7
 
0.3%
i 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1047
49.7%
o 1047
49.7%
s 7
 
0.3%
i 7
 
0.3%

toracocen
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing2958
Missing (%)73.7%
Memory size191.7 KiB
no
1054 
si
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2110
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 1054
 
26.3%
si 1
 
< 0.1%
(Missing) 2958
73.7%

Length

2023-06-29T16:03:27.365436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T16:03:27.497204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 1054
99.9%
si 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 1054
50.0%
o 1054
50.0%
s 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2110
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1054
50.0%
o 1054
50.0%
s 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2110
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1054
50.0%
o 1054
50.0%
s 1
 
< 0.1%
i 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1054
50.0%
o 1054
50.0%
s 1
 
< 0.1%
i 1
 
< 0.1%

Interactions

2023-06-29T16:03:13.005767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.226107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.612751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:13.148754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.355746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.750748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:13.279749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.485755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-29T16:03:12.873748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-29T16:03:27.629330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
n_estanciasn_visitasyearh_procedenciaapotrospaliativo_onc_nocpaliativo_no_onc_nocfiebredisneadolordeliriump_terminalagoniaps_ecogbarthelgds_fasteva_ingsedacionmedicoast_anorxagudo_establecronico_reagtransfusionparacentesistoracocentesisayuntamiento¿?toracocen
n_estancias1.0000.8610.0370.0000.0000.0000.0000.0060.0150.0000.0580.0030.0000.0000.0000.0740.0000.0380.0000.1880.0000.0000.0000.0500.0000.0000.0000.0760.000
n_visitas0.8611.0000.0270.0000.0000.0000.0490.0500.0510.0140.1530.0170.0690.0000.0000.0780.0000.1330.0510.0000.0700.0000.0160.0770.0000.0380.0000.0000.000
year0.0370.0271.0000.1020.0230.1810.0570.0190.0460.0230.0400.1120.0610.0660.0960.1840.1400.0570.1270.4190.1380.1180.1120.0390.0000.0000.2541.0001.000
h_procedencia0.0000.0000.1021.0000.3870.1500.0530.1900.0060.0280.0420.0700.0000.0650.0000.0420.0660.0000.0580.0480.0350.1920.0570.0260.0000.0000.0000.0000.000
ap0.0000.0000.0230.3871.0000.0000.0520.1630.0000.0000.0380.0420.0000.0340.0380.0630.0730.0000.1450.0610.0090.1510.0530.0470.0000.0000.0720.0000.000
otros0.0000.0000.1810.1500.0001.0000.0460.0570.0000.0000.0000.0000.0000.0000.0520.1410.0000.0000.0000.1780.1090.0910.0430.0000.0000.0000.0570.0000.000
paliativo_onc_noc0.0000.0490.0570.0530.0520.0461.0000.7560.0210.0960.2990.1430.5130.2630.4350.1600.1520.2050.1630.0320.3760.2930.7140.0470.0000.0000.0000.0000.000
paliativo_no_onc_noc0.0060.0500.0190.1900.1630.0570.7561.0000.0660.1400.1220.1090.1540.0450.1890.3140.3350.1600.0000.1440.0560.2440.7180.0000.0760.0000.0470.0000.000
fiebre0.0150.0510.0460.0060.0000.0000.0210.0661.0000.1920.0540.1200.0790.1780.0000.0690.0620.4020.0530.0170.1380.0720.0000.0000.0230.0000.1130.0000.000
disnea0.0000.0140.0230.0280.0000.0000.0960.1400.1921.0000.0930.1200.2410.2030.1590.1900.0530.0000.2220.0660.2420.1720.1210.0110.0570.0440.1370.0190.000
dolor0.0580.1530.0400.0420.0380.0000.2990.1220.0540.0931.0000.1850.2060.1810.2670.1070.1620.2640.1730.1400.3260.1590.0960.0000.0170.0000.0520.0000.000
delirium0.0030.0170.1120.0700.0420.0000.1430.1090.1200.1200.1851.0000.2450.2580.2430.3270.1450.1490.3080.1830.2100.1690.0700.0000.0080.0000.0960.0000.000
p_terminal0.0000.0690.0610.0000.0000.0000.5130.1540.0790.2410.2060.2451.0000.4130.3740.3470.0430.1360.2500.2630.4740.0000.0000.0000.0000.0000.0000.0000.000
agonia0.0000.0000.0660.0650.0340.0000.2630.0450.1780.2030.1810.2580.4131.0000.4430.3960.0550.0540.6750.1790.2690.1590.1190.0570.0000.0000.1530.0000.000
ps_ecog0.0000.0000.0960.0000.0380.0520.4350.1890.0000.1590.2670.2430.3740.4431.0000.2030.0590.0810.0870.1650.3360.2940.1010.1210.0000.0000.0600.0920.013
barthel0.0740.0780.1840.0420.0630.1410.1600.3140.0690.1900.1070.3270.3470.3960.2031.0000.1520.0700.0000.2790.3490.4340.3400.0580.0910.0800.1340.1510.000
gds_fast0.0000.0000.1400.0660.0730.0000.1520.3350.0620.0530.1620.1450.0430.0550.0590.1521.0000.0000.0000.1090.0930.1290.0710.0000.1810.0000.0710.1520.000
eva_ing0.0380.1330.0570.0000.0000.0000.2050.1600.4020.0000.2640.1490.1360.0540.0810.0700.0001.0000.1360.1260.0000.0000.0000.0000.0000.0000.0000.0000.000
sedacion0.0000.0510.1270.0580.1450.0000.1630.0000.0530.2220.1730.3080.2500.6750.0870.0000.0000.1361.0000.1140.2670.1940.0870.0310.0000.0270.1140.0000.000
medico0.1880.0000.4190.0480.0610.1780.0320.1440.0170.0660.1400.1830.2630.1790.1650.2790.1090.1260.1141.0000.4050.3270.3930.1440.0890.0000.3240.0550.000
ast_anorx0.0000.0700.1380.0350.0090.1090.3760.0560.1380.2420.3260.2100.4740.2690.3360.3490.0930.0000.2670.4051.0000.2230.0340.0100.0000.0390.1230.0450.000
agudo_estable0.0000.0000.1180.1920.1510.0910.2930.2440.0720.1720.1590.1690.0000.1590.2940.4340.1290.0000.1940.3270.2231.0000.4970.0200.0530.0000.0610.0000.000
cronico_reag0.0000.0160.1120.0570.0530.0430.7140.7180.0000.1210.0960.0700.0000.1190.1010.3400.0710.0000.0870.3930.0340.4971.0000.0000.0470.0000.0770.0000.000
transfusion0.0500.0770.0390.0260.0470.0000.0470.0000.0000.0110.0000.0000.0000.0570.1210.0580.0000.0000.0310.1440.0100.0200.0001.0000.0190.0000.0300.2270.000
paracentesis0.0000.0000.0000.0000.0000.0000.0000.0760.0230.0570.0170.0080.0000.0000.0000.0910.1810.0000.0000.0890.0000.0530.0470.0191.0000.0000.3450.0000.000
toracocentesis0.0000.0380.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0800.0000.0000.0270.0000.0390.0000.0000.0000.0001.0000.0000.0000.000
ayuntamiento0.0000.0000.2540.0000.0720.0570.0000.0470.1130.1370.0520.0960.0000.1530.0600.1340.0710.0000.1140.3240.1230.0610.0770.0300.3450.0001.0000.0000.000
¿?0.0760.0001.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0920.1510.1520.0000.0000.0550.0450.0000.0000.2270.0000.0000.0001.0000.000
toracocen0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-06-29T16:03:13.551077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-29T16:03:14.201919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-29T16:03:14.755917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

h_procedencias_procedenciaapotrosdiagnosticomotivo_ingpaliativo_onc_nocpaliativo_no_onc_nocfiebredisneadolordeliriumotros_1p_terminalagoniaps_ecogbarthelgds_fasteva_ingotros_2otros_complicacionesn_estanciasn_visitassedacionmotivo_altamedicoyearast_anorxagudo_establecronico_reagtransfusionparacentesistoracocentesisayuntamientofecha_alta¿?toracocen
0gil casaresupalnonocancer orlcontrol de sintomassisinonosinonosino40300nononoreingreso en urgencias210100noreingresovaldes2017siNaNNaNNaNNaNNaNNaNNaNNaNNaN
1clinicooncologianonomelanoma vulvarmal control dolorsinononosinonosino34nonononomet pulmonares y digest24080noexitusgalego feal2017siNaNNaNNaNNaNNaNNaNNaNNaNNaN
2clinicomirnonocancer broncogenicocontrol evolutivosisinosinonononono34400nononono14060nofin de cuidadoslopez renedo2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
3clinicodigestivononocirrosis hepaticaadmininistacion octreotidononononononononono10900nononono1010nofin de cuidadoslopez renedo2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
4clinicourgenciasnononeplasia de mama estadio IVcontrol de sintomassinonosisinonosino40300no70nono2010noexituslopez renedo2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
5nonosinoanemiatransfusionnononononononononono500nononono2020nofin de cuidadosvillar del castillo2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
6clinicooncologianonoadenoca de pulmoncontrol de sintomassinonosinonoestrenimientosino34600nononoclaud familiar domicilio13050noreingreso por urgencias a hadovalcarcel2017siNaNNaNNaNNaNNaNNaNNaNNaNNaN
7clinicohematologianonoleucemia linfatica cronicatransfusionsinononononononono12600nononono2010nofin de cuidadoslopez renedo2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
8clinicotraumatologianonofx meseta tibialinfosteosintesisnonononononomucositisnono34600nononono22060nofin de cuidadossuarez2017noNaNNaNNaNNaNNaNNaNNaNNaNNaN
9clinicourgenciasnonocancer de mamacontrol de sintomassinonosisinonosino4060nononono5030noexitussuarez2017siNaNNaNNaNNaNNaNNaNNaNNaNNaN
h_procedencias_procedenciaapotrosdiagnosticomotivo_ingpaliativo_onc_nocpaliativo_no_onc_nocfiebredisneadolordeliriumotros_1p_terminalagoniaps_ecogbarthelgds_fasteva_ingotros_2otros_complicacionesn_estanciasn_visitassedacionmotivo_altamedicoyearast_anorxagudo_establecronico_reagtransfusionparacentesistoracocentesisayuntamientofecha_alta¿?toracocen
4004clinicooncologianoNaNinfeccion respiratoriaantibioterapia IVnonosisinonoNaNNaNNaN12nonoNaNNaNNaN7030noingreso oncogomez buela2022sisinononoNaNsantiago20221225t000000nono
4005clinicomirnoNaNinsuficiencia cardiaca cronicaseguimientononononononoNaNNaNNaNno40noNaNNaNNaN8030nofin cuidadosgomez buela2022nosinononoNaNsantiago20221230t000000nono
4006clinicocgdnoNaNdiverticulitisantibioterapia IVnonononononoNaNNaNNaNnononoNaNNaNNaN6020nofin cuidadosgomez buela2022nosinononoNaNsantiago20221228t000000nono
4007noncrsiNaNneoplasia gastricacontrol de sintomassinononosinoNaNNaNNaN4010noNaNNaNNaN6040siexitusgomez buela2022sinonononoNaNsantiago20221228t000000nono
4008clinicooncologianoNaNneoplasia pulmoncontrol de sintomassinonosisinoNaNNaNNaN4040noNaNNaNNaN4040siexitusgomez buela2022sinonononoNaNsantiago20221229t000000nono
4009clinicooncologianoNaNcancer mama estadio IVcontrol de sintomassinononosinoNaNNaNNaN3440noNaNNaNNaN15070siexitusbeceiro2022nononononoNaNpontecesures20221230t000000nono
4010clinicomirnoNaNanciano fragilseguimientonosinosinonoNaNNaNNaNno3040NaNNaNNaN6040noexitusvillar del castillo2022nonosinonoNaNsilleda20221231t000000nono
4011clinicomirnoNaNoclusion intestinalcuidados paliativos avanzadossisinosisinoNaNNaNNaN4020noNaNNaNNaN21070nopaso a urgenciasfernandez benito2022sinosinonoNaNrianxo20221227t000000nono
4012clinicourgenciasnoNaNITU (infeccion del tracto urinario)antibioterapia IVnosisinonosiNaNNaNNaNno207cNaNNaNNaN11030siexitusfernandez benito2022nonosinonoNaNnoya20221230t000000nono
4013clinicooncologianoNaNglioblastomacontrol de sintomassinononosisiNaNNaNNaN4000noNaNNaNNaN11060siexitusnovo2022nononononoNaNsantiago20221230t000000nono

Duplicate rows

Most frequently occurring

h_procedencias_procedenciaapotrosdiagnosticomotivo_ingpaliativo_onc_nocpaliativo_no_onc_nocfiebredisneadolordeliriumotros_1p_terminalagoniaps_ecogbarthelgds_fasteva_ingotros_2otros_complicacionesn_estanciasn_visitassedacionmotivo_altamedicoyearast_anorxagudo_establecronico_reagtransfusionparacentesistoracocentesisayuntamientofecha_alta¿?toracocen# duplicates
5nonosinoanemia cronica no filiadatransfusionnononononononononono50nononono2010nofin cuidadosfernandez benito2018noNaNNaNNaNNaNNaNNaNNaNNaNNaN3
0clinicoendocrinologianoNaNileostomiacontrol evolutivononononononoNaNNaNNaNno800noNaNNaNNaN2020nofin de cuidadosvillar del castillo2022nonosinonoNaNsilledaNaNnono2
1clinicohematologianonolinfoma folicularadministracion inmunoglobulinassinononononoNaNNaNnono70noNaNNaNno2010nofin de cuidadosfernandez benito2021nonosinononosantiagoNaNNaNNaN2
2clinicomirnonoespondilodiscitistratamiento antibiotico IVnononononononononono1000nononono2020nofin de cuidadosvillar del castillo2019noNaNNaNNaNNaNNaNNaNNaNNaNNaN2
3clinicomirnonoespondilodiscitistratamiento antibiotico IVnononononononononono900nononono2020nofin de cuidadosvillar del castillo2019noNaNNaNNaNNaNNaNNaNNaNNaNNaN2
4cliniconeurologianonoesclerosis multiplehiponatremianonononononoNaNNaNnono500noNaNNaNno2020nofin cuidadosvillar del castillo2021nonosinononoamesNaNNaNNaN2
6nonosinodemenciarecambio pegnosinononononononono1070nonono2010nofin de cuidadosvillar del castillo2019noNaNNaNNaNNaNNaNNaNNaNNaNNaN2